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Ascent rates of 3-D fractures driven by a finite batch of buoyant fluid

Published online by Cambridge University Press:  29 December 2022

Timothy Davis*
Affiliation:
Department of Earth Sciences, University of Oxford, South Parks Road, Oxford OX1 3AN, UK
Eleonora Rivalta
Affiliation:
Dipartimento di Fisica e Astronomia “Augusto Righi”, Università di Bologna, Viale Berti Pichat 6/2, Bologna, Italy Helmholtz-Zentrum Potsdam – Deutsches GeoForschungsZentrum GFZ, Physics of Earthquakes and Volcanoes, Potsdam, Germany
Delphine Smittarello
Affiliation:
European Center for Geodynamics and Seismology, 19 Rue Josy Welter, L-7256 Walferdange, Grand Duchy of Luxembourg
Richard F. Katz
Affiliation:
Department of Earth Sciences, University of Oxford, South Parks Road, Oxford OX1 3AN, UK
*
Email address for correspondence: timothy.davis@earth.ox.ac.uk

Abstract

Propagation of fluid-filled fractures by fluid buoyancy is important in a variety of settings, from magmatic dykes and veins to water-filled crevasses in glaciers. Industrial hydro-fracturing utilises fluid-driven fractures to increase the permeability of rock formations, but few studies have quantified the effect of buoyancy on fracture pathways in this context. Analytical approximations for the buoyant ascent rate facilitate observation-based inference of buoyant effects in natural and engineered systems. Such analysis exists for two-dimensional fractures, but real fractures are three-dimensional (3-D). Here we present novel analysis to predict the buoyant ascent speed of 3-D fractures containing a fixed-volume batch of fluid. We provide two estimates of the ascent rate: an upper limit applicable at early time, and an asymptotic estimate (proportional to $t^{-2/3}$) describing how the speed decays at late time. We infer and verify these predictions by comparison with numerical experiments across a range of scales and analogue experiments on liquid oil in solid gelatine. We find the ascent speed is a function of the fluid volume, density, viscosity and the elastic parameters of the host medium. Our approximate solutions predict the ascent rate of fluid-driven fractures across a broad parameter space, including cases of water injection in shale and magmatic dykes. Our results demonstrate that in the absence of barriers or fluid loss, both dykes and industrial hydro-fractures can ascend by buoyancy over a kilometre within a day. We infer that barriers and fluid loss must cause the arrest of ascending fractures in industrial settings.

Information

Type
JFM Papers
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press
Figure 0

Figure 1. Numerical simulation of an ascending, finite batch of fluid. In this simulation, $1.95\ {\rm m}^3$ of fluid is injected over 130 s. Two views of the fracture are shown at each time. On the left-hand side is a view looking onto the fracture's face, shaded by aperture ($w$) with the tip-line in black. On the right-hand side is a grey cross-section showing the profile of the aperture along the centre of the crack. The latter has a horizontal exaggeration of $2\times 10^4$. (a) Analytical solution that defines the radius $a$. The plotted solution contains fluid volume $V$ and has a radius such that $K_I(z=-a)=0$. (b) Numerical simulation of the fracture ascent using PyFrac (table 1). Simulation times are shown below the respective fractures. The time-dependant analytical approximation of the fracture's cross-section is shown as dashed lines (Roper & Lister 2007). The tail height $h$ from (3.10) is marked with a dot.

Figure 1

Table 1. Parameter values used for the simulation shown in figures 1 and 2. These correspond to simulation-case II from the main text of Salimzadeh et al. (2020).

Figure 2

Figure 2. Upper-tip ascent speed from the PyFrac simulation (solid line) and the predicted asymptotes and analytical approximations (broken lines). Times corresponding to steps in figure 1 are shown as asterisks. Parameter values are given in table 1. (a) Ascent rate versus time on a log–log plot. (b) Ascent rate versus height. Our prediction for the maximum ascent speed from (3.7) is shown as a horizontal dashed line. Also shown is the analytical approximation of the front speed produced using (3.10) and (3.11) (dash–dot line).

Figure 3

Figure 3. Numerical versus analytical (3.11) speed estimates at times $t$ since injection. Plotted are nine simulations performed using the code of Zia & Lecampion (2020) and summarised in table 2. These comprise three parametric cases, each with three different injection volumes and rates. The plots stop at the point when the simulated fracture reached the edge of the meshed domain or Pyfrac terminated the simulation.

Figure 4

Figure 4. Numerical versus analytical (3.10) height estimates at times $t$ since injection. Height here is defined as the distance from the injection point to the upper tip. This plot uses the same numerical solutions as in figure 3.

Figure 5

Table 2. Properties used to numerically simulate fracture ascent for different physical processes. The injection rate for such cracks is set to $t_I=(V \, \tilde {v}_V)/a$, such that the injection is complete before the fracture ascends past $h=2a$. Note this results in $\tilde {v}_{V}/v_r$ to $\sim$2 for all results. Here $t_I/t_{mk}$ for $V_I/V_c=2$, 10 and 100 are $1.7\times 10^{4}$, 45 and $2\times 10^{-2}$, respectively; as such these span both viscous and toughness dominated radial growth regimes at the point at which the injection stops.

Figure 6

Figure 5. Upper tip height from analogue experiments where silicon oil is injected into gelatine solids. Parameter values are given in table 3. (a) Height versus time, showing that at late times, curves approach the $t^{1/3}$ asymptote (dash–dot). The triangles denote the end of injection ($t_I$). (b) The height from the experiments is plotted against the predicted speed using (3.11), where the time is that elapsed since the start of the injection. The predicted heights have been translated such that they are equal to the observed height at $t=2 t_I$ (squares).

Figure 7

Table 3. Properties of the analogue gelatine experiments of Smittarello (2019). Experiment reference numbers are shown in the first row. Note that the injection rates were not recorded. Note here the reported values of $K_c$ are estimated using an empirical formula based on the shear modulus, reported in § 3.1 of Davis et al. (2020).

Figure 8

Figure 6. Comparison of different mesh sizes and schemes. Here $30 \times 30$ refers to the number of cells in a square of size $0.75a$. Moving-mean averaging is applied to the front-speed in the plots, to remove fluctuations in speed. The sample size of 40 was been used.